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09.04.2022 | Original Article

A Physics-informed and data-driven deep learning approach for wave propagation and its scattering characteristics

verfasst von: Soo Young Lee, Choon-Su Park, Keonhyeok Park, Hyung Jin Lee, Seungchul Lee

Erschienen in: Engineering with Computers | Ausgabe 4/2023

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Abstract

Understanding the propagation of waves and their scattering characteristics is critical in various scientific and engineering domains. While the majority of present work is based on numerical approaches, their high computational cost and discontinuity in the entire engineering workflow raise the need to resolve obstacles for fully utilizing the methods in an interactive and end-to-end manner. In this study, we propose a deep learning approach that can simulate the wave propagation and scattering phenomena precisely and efficiently. In particular, we present methods of incorporating physics-based knowledge into the deep learning framework to give the learning process strong inductive biases regarding wave propagation and scattering behaviors. We demonstrate that the proposed method can successfully produce physically valid wave field trajectories induced by random scattering objects. We show that the proposed physics-informed strategy exhibits significantly improved prediction results than purely data-driven methods through quantitative and qualitative evaluation from various angles. Subsequently, we assess the computational efficiency of the proposed method as a neural engine, showing that the proposed approach can significantly accelerate the scientific simulation process compared to the numerical method. Our study delivers the potential of the proposed physics-informed approach to be utilized for real-time, accurate, and interactive scientific analyses in a wide variety of engineering and application disciplines.

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Metadaten
Titel
A Physics-informed and data-driven deep learning approach for wave propagation and its scattering characteristics
verfasst von
Soo Young Lee
Choon-Su Park
Keonhyeok Park
Hyung Jin Lee
Seungchul Lee
Publikationsdatum
09.04.2022
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 4/2023
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-022-01640-7